Autonomous vehicles are making headways, just not in the way the world expected.
Be it Motional’s new electric IONIQ 5-based robotaxi delivering its first ride with Lyft or Toyota’s Boshoku already in use for rideshares in Japan or as a rented room for meetings, we’ve seen many new use cases of autonomous vehicles, and specifically, a new emphasis on shared mobility. For example, geographically constrained autonomous shuttles are ideal for city infrastructures where reducing carbon emissions is a goal. Progress on public transit is also being made, as seen with ZF and Beep autonomous shuttles and others, but more development is needed before they are commercially viable.
Another development is BMW’s newest concept for an electric vehicle, introduced as the “i Vision Dee.” The car focuses on more than just self-driving and instead is about giving the driver the information they need at that moment. BMW announced production of the design will start in 2025. Separately, NVIDIA announced its partnership with Foxconn, who will incorporate NVIDIA’s DRIVE Orin chip and DRIVE Hyperion sensors in its autonomous vehicle platforms to speed up its time-to-market and time-to-cost strategies.
Sensors are also playing a critical role in modern vehicles. Bosch has been a big player in MEMS sensors, LiDAR, and image sensors and recently introduced the RideCare Companion solution that aims to increase safety while ridesharing. Other examples include improved capabilities in LiDAR sensors like longer range from Mobileye and a substantial cost reduction from Innoviz Technologies.
Then, you have autonomous driving systems being used in industrial applications such as trucks for farming as well as others like racing, mining, or boats. Similar to shared mobility, the challenge of quickly commercializing these vehicles is on everyone’s mind. Recent industry collaboration, such as Continental and Ambarella as well as Magna and LG, will help advance the integration of hardware and software solutions going forward.